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Surface melt on the Greenland ice sheet has been increasing in intensity and extent over the last decades due to Arctic atmospheric warming. Surface melt depends on the surface energy balance, which includes the atmospheric forcing but also the thermal budget of the snow, firn and ice near the ice sheet surface. The temperature of the ice sheet subsurface has been used as an indicator of the thermal state of the ice sheet's surface. Here, we present a compilation of 4612 measurements of firn and ice temperature at 10m below the surface (T10m) across the ice sheet, spanning from 1912 to 2022. The measurements are either instantaneous or monthly averages. We train an artificial neural network model (ANN) on 4597 of these point observations, weighted by their relative representativity, and use it to reconstruct T10m over the entire Greenland ice sheet for the period 1950-2022 at a monthly timescale. We use 10-year averages and mean annual values of air temperature and snowfall from the ERA5 reanalysis dataset as model input. The ANN indicates a Greenland-wide positive trend of T10m at 0.2°C per decade during the 1950-2022 period, with a cooling during 1950-1985 (-0.4°C per decade) followed by a warming during 1985-2022 (+0.7° per decade). Regional climate models HIRHAM5, RACMO2.3p2 and MARv3.12 show mixed results compared to the observational T10m dataset, with mean differences ranging from -0.4°C (HIRHAM) to 1.2°C (MAR) and root mean squared differences ranging from 2.8°C (HIRHAM) to 4.7°C (MAR). The observation-based ANN also reveals an underestimation of the subsurface warming trends in climate models for the bare-ice and dry-snow areas. The subsurface warming brings the Greenland ice sheet surface closer to the melting point, reducing the amount of energy input required for melting. Our compilation documents the response of the ice sheet subsurface to atmospheric warming and will enable further improvements of models used for ice sheet mass loss assessment and r

The firn layer on the Greenland Ice Sheet (GrIS) plays a crucial role in buffering surface meltwater runoff, which is constrained by the available firn pore space and impermeable ice layers that limit deeper meltwater percolation. Understanding these firn properties is essential for predicting current and future meltwater runoff and its contribution to global sea-level rise. While very-high-frequency (VHF) radars have been extensively used for surveying the GrIS, their lower bandwidth restricts direct firn stratigraphy extraction. In this study, we use concurrent VHF airborne radar and laser altimetry data collected as part of Operation IceBridge over the 2011-2019 period to investigate our hypothesis that vertical heterogeneities in firn (i.e. ice layers) cause vertical offsets in the radar surface reflection (dz). Our results, corroborated by modelling and firn core analyses, show that a dz larger than 1m is strongly related to the vertical heterogeneity of a firn profile and effectively delineates between vertically homogeneous and vertically heterogeneous firn profiles over a depth range of ∼4m. Temporal variations in dz align with climatic events and reveal an expansion of heterogeneous firn between 2011-2013 covering an area of ∼350815km2, followed by firn replenishment over the years 2014-2019 spanning an area of ∼667725km2. Our approach reveals the firn evolution of key regions on the Greenland Ice Sheet, providing valuable insights for detecting potential alterations in meltwater runoff patterns.

Camp Century is an American military base built in 1959 under the surface of the Greenland ice sheet and decommissioned in 1967. Here, we use outputs from RACMO2.3p2 and CanESM2 climate models, adjusted to meteorological observations, and a firn model to simulate the firn density and temperature at Camp Century between 1966 and 2100. The model output is evaluated against an extensive set of firn 3observations and three Representative Concentration Pathways (RCP2.6, 4.5 and 8.5) are considered as future scenarios. Our model suggests that the upper horizon of the Camp Century debris field - observed at a depth of 32 m in 2017 - will continue to be buried by persistent net accumulation over the next eighty years under all RCP scenarios. This horizon depth will be between 58 and 64 m in 2100, depending on the RCP scenario. We estimate a maximum meltwater percolation depth of 1.1 m under all RCP scenarios. We therefore find it extremely unlikely that surface meltwater interacts with the subsurface debris field at Camp Century before 2100 under all RCP scenarios. Camp Century's future is representative of the firn area in northwestern Greenland, bound to shift from dry snow to a percolation regime. Our model suggests that 10 m firn temperatures at Camp Century will increase from -24.0 degrees C in 1966 to -21.3, -20.0 and -18.6 degrees C in 2100 under the RCP2.6, 4.5 and 8.5 scenarios, respectively. We reveal a previously unknown warm bias in air temperatures simulated at Camp Century by both RACMO2.3p2 and CanESM2 climate models which needs to be accounted for when using these models to predict melt, firn evolution and sea-level contribution of the Greenland ice sheet. We also present novel in situ measurements of firn compaction rates, which indicate that about 25% of firn compaction of the top 62 m of firn occurs below 20 m depth. This highlights the importance of deep-firn compaction measurements for model evaluation and correction of altimetry products.

Antarctic surface mass balance (SMB) is largely determined by precipitation over the continent and subject to regional climate variability related to the Southern Annular Mode (SAM) and other climatic drivers at the large scale. Locally however, firn and snowpack processes are important in determining SMB and the total mass balance of Antarctica and global sea level. Here, we examine factors that influence Antarctic SMB and attempt to reconcile the outcome with estimates for total mass balance determined from the GRACE satellites. This is done by having the regional climate model HIRHAM5 forcing two versions of an offline subsurface model, to estimate Antarctic ice sheet (AIS) SMB from 1980 to 2017. The Lagrangian subsurface model estimates Antarctic SMB of 2473.5±114.4 Gt yr−1, while the Eulerian subsurface model variant results in slightly higher modelled SMB of 2564.8±113.7 Gt yr−1. The majority of this difference in modelled SMB is due to melt and refreezing over ice shelves and demonstrates the importance of firn modelling in areas with substantial melt. Both the Eulerian and the Lagrangian SMB estimates are within uncertainty ranges of each other and within the range of other SMB studies. However, the Lagrangian version has better statistics when modelling the densities. Further, analysis of the relationship between SMB in individual drainage basins and the SAM is carried out using a bootstrapping approach. This shows a robust relationship between SAM and SMB in half of the basins (13 out of 27). In general, when SAM is positive there is a lower SMB over the plateau and a higher SMB on the westerly side of the Antarctic Peninsula, and vice versa when the SAM is negative. Finally, we compare the modelled SMB to GRACE data by subtracting the solid ice discharge, and we find that there is a good agreement in East Antarctica but large disagreements over the Antarctic Peninsula. There is a large difference between published estimates of dis

Perennial snow, or firn, covers 80 % of the Greenland ice sheet and has the capacity to retain surface meltwater, influencing the ice sheet mass balance and contribution to sea-level rise. Multilayer firn models are traditionally used to simulate firn processes and estimate meltwater retention. We present, intercompare and evaluate outputs from nine firn models at four sites that represent the ice sheet's dry snow, percolation, ice slab and firn aquifer areas. The models are forced by mass and energy fluxes derived from automatic weather stations and compared to firn density, temperature and meltwater percolation depth observations. Models agree relatively well at the dry-snow site while elsewhere their meltwater infiltration schemes lead to marked differences in simulated firn characteristics. Models accounting for deep meltwater percolation overestimate percolation depth and firn temperature at the percolation and ice slab sites but accurately simulate recharge of the firn aquifer. Models using Darcy's law and bucket schemes compare favorably to observed firn temperature and meltwater percolation depth at the percolation site, but only the Darcy models accurately simulate firn temperature and percolation at the ice slab site. Despite good performance at certain locations, no single model currently simulates meltwater infiltration adequately at all sites. The model spread in estimated meltwater retention and runoff increases with increasing meltwater input. The highest runoff was calculated at the KAN_U site in 2012, when average total runoff across models (+/- 2 sigma) was 353 +/- 610 mm w. e. (water equivalent), about 27 +/- 48 % of the surface meltwater input. We identify potential causes for the model spread and the mismatch with observations and provide recommendations for future model development and firn investigation.

Perennial snow, or firn, covers 80 % of the Greenland ice sheetand has the capacity to retain surface meltwater, influencing the ice sheetmass balance and contribution to sea-level rise. Multilayer firn models aretraditionally used to simulate firn processes and estimate meltwaterretention. We present, intercompare and evaluate outputs from nine firnmodels at four sites that represent the ice sheet's dry snow, percolation,ice slab and firn aquifer areas. The models are forced by mass and energyfluxes derived from automatic weather stations and compared to firn density,temperature and meltwater percolation depth observations. Models agreerelatively well at the dry-snow site while elsewhere their meltwaterinfiltration schemes lead to marked differences in simulated firncharacteristics. Models accounting for deep meltwater percolation overestimate percolation depth and firn temperature at the percolation andice slab sites but accurately simulate recharge of the firn aquifer. Modelsusing Darcy's law and bucket schemes compare favorably to observed firntemperature and meltwater percolation depth at the percolation site, butonly the Darcy models accurately simulate firn temperature and percolationat the ice slab site. Despite good performance at certain locations, nosingle model currently simulates meltwater infiltration adequately at allsites. The model spread in estimated meltwater retention and runoffincreases with increasing meltwater input. The highest runoff was calculatedat the KAN_U site in 2012, when average total runoff acrossmodels (±2σ) was 353±610 mm w.e. (water equivalent), about 27±48 % of the surface meltwater input. We identify potential causes for themodel spread and the mismatch with observations and provide recommendationsfor future model development and firn investigation.

In recent decades, the Greenland ice sheet (GrIS) has frequently experienced record melt events, which have significantly affected surface mass balance (SMB) and estimates thereof. SMB data are derived from remote sensing, regional climate models (RCMs), firn cores and automatic weather stations (AWSs). While remote sensing and RCMs cover regional scales with extents ranging from 1 to 10 km, AWS data and firn cores are point observations. To link regional scales with point measurements, we investigate the spatial variability of snow accumulation (bs) within areas of approximately 1–4 km2 and its temporal changes within 2 years of measurements. At three different sites on the southwestern GrIS (Swiss Camp, KAN-U, DYE-2), we performed extensive ground-penetrating radar (GPR) transects and recorded multiple snow pits. If the density is known and the snowpack dry, radar-measured two-way travel time can be converted to snow depth and bs. We spatially filtered GPR transect data to remove small-scale noise related to surface characteristics. The combined uncertainty of bs from density variations and spatial filtering of radar transects is at 7 %–8 % per regional scale of 1–4 km2. Snow accumulation from a randomly selected snow pit is very likely representative of the regional scale of 1–4 km2 (with probability p=0.8 for a value within 10 % of the regional mean for KAN-U, and p>0.95 for Swiss Camp and DYE-2). However, to achieve such high representativeness of snow pits, it is required to determine the average snow depth within the vicinity of the pits. At DYE-2, the spatial pattern of snow accumulation was very similar for 2 consecutive years. Using target reflectors placed at respective end-of-summer-melt horizons, we additionally investigated the occurrences of lateral redistribution within one melt season. We found no evidence of lateral flow of meltwater in the current climate at DYE-2. Such studies of spatial representativeness and temporal changes in accumulation are

Current sea-level rise partly stems from increased surface melting and meltwater runoff from the Greenland ice sheet. Multi-year snow, also known as firn, covers about 80% of the ice sheet and retains part of the surface meltwater. Since the firn cold content integrates its physical and thermal characteristics, it is a valuable tool for determining the meltwater-retention potential of firn. We use gap-filled climatological data from nine automatic weather stations in the ice-sheet accumulation area to drive a surface-energy-budget and firn model, validated against firn density and temperature observations, over the 1998-2017 period. Our results show a stable top 20 m firn cold content (CC20) at most sites. Only at the lower-elevation Dye-2 site did CC20 decrease, by 24% in 2012, before recovering to its original value by 2017. Heat conduction towards the surface is the main process feeding CC20 at all nine sites, while CC20 reduction occurs through low-cold-content fresh-snow addition at the surface during snowfall and latent-heat release when meltwater refreezes. Our simulations suggest that firn densification, while reducing pore space for meltwater retention, increases the firn cold content, enhances near-surface meltwater refreezing and potentially sets favourable conditions for ice-slab formation., SCOPUS: ar.j, info:eu-repo/semantics/published

The perennial snow, or firn, on the Greenland ice sheet each summer stores part of the meltwater formed at the surface, buffering the ice sheet’s contribution to sea level. We gathered observations of firn air content, indicative of the space available in the firn to retain meltwater, and find that this air content remained stable in cold regions of the firn over the last 65 years but recently decreased significantly in western Greenland.

The firn covering the Greenland ice sheet interior can retain part of the surface melt, buffering the ice sheet’s contribution to sea level, but its characteristics are still little known. Using remote-sensing observations from 2000- 2017, we estimate that firn covers 1,405,500 ±17,250 km2 of the ice sheet. We present 344 firn-core-derived observations of the top 10 m firn air content (FAC10), indicative of the firn’s meltwater retention capacity. FAC10 remained stable in the coldest 74% of the firn area during 1953-2017, while FAC10 decreased in the warmest and driest 12% of the firn area between 1997-2008 and 2011-2017, resulting in a loss of 180 ±78 km3 (-26 ±11%) of air from the near-surface firn.

The extent to which snow and ice surfaces undergo melt is mainly dictated by their absorption of shortwave radiation, and thus the surface albedo is a critical parameter for accurately modelling the surface energy balance and mass balance of the Greenland Ice Sheet (GrIS). Multiple variables affect the surface albedo, including the atmospheric conditions, the solar zenith angle (SZA), and the snowpack’s characteristics. This thesis uses output from a firn evolution model (Vandecrux et al., in review) to explain 8 years of albedo observations from the Kangerlussuaq Upper (KAN_U) automatic weather station (PROMICE; Ahlstrøm et al., 2008). A parameterised model is developed that differentiates between three snow conditions (aged, refrozen, and wet), and uses four variables as input: the SZA; the Sky-Air Temperature Ratio (SATR); the Liquid Water Content (LWC); and the modelled grain size. When compared to the observed albedo at KAN_U, the model could explain more than 60% of the observed albedo variation for 2009, 2010, 2011 and 2015, but this fell below 30% for 2014. So far, no reasons for these differences have been uncovered. Each of the three snow conditions performed approximately equally well, and only the lightly cloudy skies were seen to be identifiable worse compared to any other cloud cover. The model was also largely consistent between months, with the exception of October, when the poor fit is attributed to the very short days and consistently high SZAs. An immediate priority is to test the model against albedo observations from other PROMICE datasets. It is unclear how applicable the model will be to other locations, because it explicitly replicates a rising albedo from the early morning to noon. This was consistently seen in the observational data at KAN_U but contradicts the accepted theory. However, it is possible that the approach used by this parameterisation could be easily modified and tuned to alternative locations.

The surface snow density of glaciers and ice sheets is of fundamental importance in converting volume to mass in both altimetry and surface mass balance studies, yet it is often poorly constrained. Site-specific surface snow densities are typically derived from empirical relations based on temperature and wind speed. These parameterizations commonly calculate the average density of the top meter of snow, thereby systematically overestimating snow density at the actual surface. Therefore, constraining surface snow density to the top 0.1 m can improve boundary conditions in high-resolution firn-evolution modeling. We have compiled an extensive dataset of 200 point measurements of surface snow density from firn cores and snow pits on the Greenland ice sheet. We find that surface snow density within 0.1 m of the surface has an average value of 315 kg m−3 with a standard deviation of 44 kg m−3, and has an insignificant annual air temperature dependency. We demonstrate that two widely-used surface snow density parameterizations dependent on temperature systematically overestimate surface snow density over the Greenland ice sheet by 17–19%, and that using a constant density of 315 kg m−3 may give superior results when applied in surface mass budget modeling.

The surface snow density of glaciers and ice sheets is of fundamental importance in converting volume to mass in both altimetry and surface mass balance studies, yet it is often poorly constrained. Site-specific surface snow densities are typically derived from empirical relations based on temperature and wind speed. These parameterizations commonly calculate the average density of the top meter of snow, thereby systematically overestimating snow density at the actual surface. Therefore, constraining surface snow density to the top 0.1 m can improve boundary conditions in high-resolution firn-evolution modeling. We have compiled an extensive dataset of 200 point measurements of surface snow density from firn cores and snow pits on the Greenland ice sheet. We find that surface snow density within 0.1 m of the surface has an average value of 315 kg m−3 with a standard deviation of 44 kg m−3, and has an insignificant annual air temperature dependency. We demonstrate that two widely- used surface snow density parameterizations dependent on temperature systematically overestimate surface snow density over the Greenland ice sheet by 17–19%, and that using a constant density of 315 kg m−3 may give superior results when applied in surface mass budget modeling.

Recent Arctic atmospheric warming induces more frequent surface melt in the accumulation area of the Greenland ice sheet. This increased melting modifies the near‐surface firn structure and density and may reduce the firn's capacity to retain meltwater. Yet, few long‐term observational records are available to determine the evolution and drivers of firn density. In this study, we compile and gap‐fill Greenland Climate Network (GC‐Net) automatic weather station data from Crawford Point, Dye‐2, NASA‐SE and Summit between 1998 and 2015. These records then force a coupled surface energy balance and firn evolution model. We find at all sites except Summit that increasing summer turbulent heat fluxes to the surface are compensated by decreasing net radiative fluxes. After evaluating the model against firn cores, we find that, starting from 2006, the density of the top 20 m of firn at Dye‐2 increased by 11%, decreasing the pore volume by ‐18%. Crawford Point and Summit show stable near‐surface firn density over 1998‐2000 and 2000‐2015 respectively while we calculate a ‐4% decrease of firn density at NASA‐SE over 1998‐2015. For each year, the model identifies the drivers of density change in the top 20 m firn and quantifies their contributions. The key driver, snowfall, explains alone 72 to 92% of the variance in day‐to‐day change in firn density while melt explains from 7 to 33 %. Our result indicates that correct estimates of the magnitude and variability of precipitation are necessary to interpret or simulate the evolution of the firn.

The surface snow density of glaciers and ice sheets is of fundamental importance in converting volume to mass in both altimetry and surface mass balance studies, yet it is often poorly constrained. Site-specific surface snow densities are typically derived from empirical relations based on temperature and wind speed. These parameterizations commonly calculate the average density of the top meter of snow, thereby systematically overestimating snow density at the actual surface. Therefore, constraining surface snow density to the top 0.1 m can improve boundary conditions in high-resolution firn-evolution modeling. We have compiled an extensive dataset of 200 point measurements of surface snow density from firn cores and snow pits on the Greenland ice sheet. We find that surface snow density within 0.1 m of the surface has an average value of 315 kg m−3 with a standard deviation of 44 kg m−3, and has an insignificant annual air temperature dependency. We demonstrate that two widely-used surface snow density parameterizations dependent on temperature systematically overestimate surface snow density over the Greenland ice sheet by 17–19%, and that using a constant density of 315 kg m−3 may give superior results when applied in surface mass budget modeling.

The repeat acquisition of high-resolution snow depth measurements has important research and civil applications in the Arctic. Currently the surveying methods for capturing the high spatial and temporal variability of the snowpack are expensive, in particular for small areal extents. An alternative methodology based on Unmanned Aerial Systems (UASs) and digital photogrammetry was tested over varying surveying conditions in the Arctic employing two diverse and low-cost UAS-camera combinations (500 and 1700 USD, respectively). Six areas, two in Svalbard and four in Greenland, were mapped covering from 1386 to 38,410 m2. The sites presented diverse snow surface types, underlying topography and light conditions in order to test the method under potentially limiting conditions. The resulting snow depth maps achieved spatial resolutions between 0.06 and 0.09 m. The average difference between UAS-estimated and measured snow depth, checked with conventional snow probing, ranged from 0.015 to 0.16 m. The impact of image pre-processing was explored, improving point cloud density and accuracy for different image qualities and snow/light conditions. Our UAS photogrammetry results are expected to be scalable to larger areal extents. While further validation is needed, with the inclusion of extra validation points, the study showcases the potential of this cost-effective methodology for high-resolution monitoring of snow dynamics in the Arctic and beyond.